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Indonesia resides on most earthquake region with more than 100 active volcanoes, and high number of seismic activities per year. In order to reduce the casualty, some method to predict earthquake have been developed to estimate the seismic movement. However, most prediction use only short term of historical data to predict the incoming earthquake, which has limitation on model performance. This work uses medium to long term earthquake historical data from around the world to detect and identify the possibility of seismic activity in Indonesia. This paper focuses on earthquakes within Indonesia region that were recorded by 2 local government bodies and 8 legitimate international sources. We make an estimation of a mediumto-long term prediction via Machine Learning algorithms, which are Multinomial Logistic Regression, Support Vector Machine and Na¨ive Bayes, and compares their performance. To obtain our results, we categorize our work into four stages, which are data collection, data cleansing, prediction and result analysis.To demonstrate this result in a dynamic form, we present our work in a web based environment that connected to an analytic engine to perform the prediction and visualization.This work shows that the Support Vector Machine outperforms other method. We compare the Root Mean Square Error computation results that lead us into how concentrated data is around the line of best fit, where the Multinomial Logistic Regression is 0.777, Na¨ive Bayes is 0.922 and Support Vector Machine is 0.751. In predicting future earthquake, Support Vector Machine outperforms other two methods that produce significant distance and magnitude to current earthquake report. Regarding the volume of data, this work should also be categorized as a Big Data project.

Comparison of machine learning performance for earthquake prediction in Indonesia using 30 years historical data